Academic Journal
MS-HIENet: multi-scale hybrid implicit-explicit registration network.
| Τίτλος: | MS-HIENet: multi-scale hybrid implicit-explicit registration network. |
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| Συγγραφείς: | Wang Z; School of Control Science and Engineering, Shandong University, Jinan, Shandong Province, People's Republic of China., Liu X; School of Control Science and Engineering, Shandong University, Jinan, Shandong Province, People's Republic of China., Chen X; School of Control Science and Engineering, Shandong University, Jinan, Shandong Province, People's Republic of China., Wei Y; School of Control Science and Engineering, Shandong University, Jinan, Shandong Province, People's Republic of China.; Shandong Research Institute of Industrial Technology, Jinan, Shandong Province, People's Republic of China. |
| Πηγή: | Physics in medicine and biology [Phys Med Biol] 2026 Feb 25; Vol. 71 (4). Date of Electronic Publication: 2026 Feb 25. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: IOP Publishing Country of Publication: England NLM ID: 0401220 Publication Model: Electronic Cited Medium: Internet ISSN: 1361-6560 (Electronic) Linking ISSN: 00319155 NLM ISO Abbreviation: Phys Med Biol Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Bristol : IOP Publishing |
| Ιατρικοί όροι (MeSH): | Image Processing, Computer-Assisted*/methods, Lung/diagnostic imaging ; Convolutional Neural Networks ; Humans ; Tomography, X-Ray Computed |
| Περίληψη: | Objective.Medical image registration serves as a cornerstone for precision diagnosis and treatment, particularly for dynamic organs like the lungs, where high deformability and complex motion patterns impose significant challenges. Traditional iterative methods are time-consuming, while existing deep learning approaches often struggle to synergistically optimize large deformation modeling and localized fine structure preservation due to limited receptive fields. This study aims to address the challenge of jointly handling large-scale and localized deformations in pulmonary imaging by developing a novel deep learning framework.Approach.We propose a multi-scale hybrid implicit-explicit registration network (MS-HIENet), a mask-free end-to-end framework that integrates implicit neural representation (INR) and convolutional neural networks (CNN). The method employs a two-fold strategy: First, a multi-scale optimization mechanism where low-resolution layers leverage INR to capture global deformations and high-resolution layers utilize CNN to refine local anatomical structures, enabling coarse-to-fine hierarchical registration. Second, an INR-based coordinate-to-displacement implicit mapping framework is used to directly model continuous deformation fields, eliminating the dependency on mask annotations.Main results.Experimental results on the deformable image registration laboratory dataset demonstrate that MS-HIENet achieves a mean target registration error of 1.00 mm, representing an average reduction of 29.5% compared to state-of-the-art deep learning methods. Ablation studies validate the effectiveness of the multi-scale collaboration and hybrid implicit-explicit representation, with the deformation field folding rate reaching an minimal level (mean: 0.00017).Significance.The proposed method effectively bridges the gap between global deformation consistency and local anatomical precision. By combining the continuous modeling capabilities of INR with the local feature refinement of CNNs, MS-HIENet significantly enhances topological consistency and clinical applicability, offering a robust solution for high-precision lung image analysis. (© 2026 Institute of Physics and Engineering in Medicine. All rights, including for text and data mining, AI training, and similar technologies, are reserved.) |
| Contributed Indexing: | Keywords: deformable registration; implicit neural representation; lung CT; multi-scale |
| Entry Date(s): | Date Created: 20260213 Date Completed: 20260707 Latest Revision: 20260707 |
| Update Code: | 20260708 |
| DOI: | 10.1088/1361-6560/ae45e7 |
| PMID: | 41687255 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1361-6560 |
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| DOI: | 10.1088/1361-6560/ae45e7 |